library(MASS)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.7 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ dplyr::select() masks MASS::select()
library(ggplot2)
library(readr)
library(viridis)
Loading required package: viridisLite
library(ggfortify)
library(bbmle) #For ICtab
Loading required package: stats4
Attaching package: ‘bbmle’
The following object is masked from ‘package:dplyr’:
slice
library(car)
Loading required package: carData
Attaching package: ‘car’
The following object is masked from ‘package:dplyr’:
recode
The following object is masked from ‘package:purrr’:
some
library(emmeans)
round_any <- function(x, accuracy, f=round){f(x/ accuracy) * accuracy}
round_any <- function(x, accuracy, f=round){f(x/ accuracy) * accuracy}
trial_types <- c("none","brood","worker","queen","all")
for (t in trial_types){
bee_param_df <- list.files(path=paste0("hive_data/heat_",t,"/"), full.names = TRUE) %>%
lapply(read_csv, show_col_types = FALSE) %>%
bind_rows
write.csv(bee_param_df,paste0("bee_data_isaac_heat_",t,".csv"),row.names = FALSE)
}
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What these all mean: n = queen cells per hour rb = brood radius rn = necter radius w = total daily honey pph = pollen ratio ph = honey consumption ratio pp = pollen consumption ratio k = consumption probability value
broodMetric = average number of brood surrounding other brood pollenRing = average min distance between honey and brood
brood_bee_param <- read.csv("bee_data_isaac_heat_brood.csv") %>% mutate(type = "Brood") %>% mutate(wha = 0, qha = 0)
worker_bee_param <- read.csv("bee_data_isaac_heat_worker.csv") %>% mutate(type = "Worker") %>% mutate(bhd = 0, qha = 0)
queen_bee_param <- read.csv("bee_data_isaac_heat_queen.csv") %>% mutate(type = "Queen") %>% mutate(bhd = 0, wha = 0)
none_bee_param <- read.csv("bee_data_isaac_heat_none.csv") %>% mutate(type = "None") %>% mutate(bhd = 0,wha = 0, qha = 0)
all_bee_param <- read.csv("bee_data_isaac_heat_all.csv") %>% mutate(type = "All")
bee_heat_param_df <- rbind(brood_bee_param,worker_bee_param) %>% rbind(.,queen_bee_param) %>% rbind(.,all_bee_param) %>% rbind(.,none_bee_param)
ggplot(bee_heat_param_df, aes(x = type, y = pBroodHeat))+
geom_boxplot()+
theme_classic()
heat_glm <- glm(pBroodHeat ~ 0 + type, data = bee_heat_param_df)
contrast(emmeans(heat_glm, "type"), "pairwise", adjust = "Tukey")
contrast estimate SE df t.ratio p.value
All - Brood -0.0545 0.00847 3077 -6.429 <.0001
All - None -0.4252 0.02545 3077 -16.704 <.0001
All - Queen -0.2398 0.00847 3077 -28.298 <.0001
All - Worker -0.3607 0.00847 3077 -42.557 <.0001
Brood - None -0.3707 0.02530 3077 -14.654 <.0001
Brood - Queen -0.1853 0.00799 3077 -23.195 <.0001
Brood - Worker -0.3062 0.00799 3077 -38.320 <.0001
None - Queen 0.1854 0.02530 3077 7.328 <.0001
None - Worker 0.0645 0.02530 3077 2.550 0.0801
Queen - Worker -0.1208 0.00799 3077 -15.125 <.0001
P value adjustment: tukey method for comparing a family of 5 estimates
ggplot(all_bee_param %>% na.omit(), aes(x = qha, y = bhd, color = pBroodHeat))+
geom_point()+
scale_color_viridis()+
theme_classic()
rounded_all_bee_param <- all_bee_param %>% mutate(rounded_qha = round_any(qha, 0.25),
rounded_bhd = round_any(bhd, 0.25))
ggplot(rounded_all_bee_param %>% na.omit(), aes(x = rounded_qha, y = rounded_bhd, z = pBroodHeat))+
geom_contour_filled()+
theme_classic()
NA
NA
Looking at correlation between heat parameter and brood in the place
ggplot(bee_heat_param_df %>% filter(wha > 0), aes(x = wha, y = pBroodHeat))+
geom_point()+
geom_smooth(method = 'glm', formula = 'y ~ x')+
theme_classic()
cor.test(worker_bee_param$wha,worker_bee_param$pBroodHeat)
Pearson's product-moment correlation
data: worker_bee_param$wha and worker_bee_param$pBroodHeat
t = -16.883, df = 798, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.5623235 -0.4600492
sample estimates:
cor
-0.5130047
ggplot(bee_heat_param_df %>% filter(qha > 0), aes(x = qha, y = pBroodHeat))+
geom_point()+
geom_smooth(method = 'glm', formula = 'y ~ x')+
theme_classic()
cor.test(queen_bee_param$qha,queen_bee_param$pBroodHeat)
Pearson's product-moment correlation
data: queen_bee_param$qha and queen_bee_param$pBroodHeat
t = -43.92, df = 798, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.8602165 -0.8195107
sample estimates:
cor
-0.8410501
Let’s make some stepwise models! Just with the all data for now so there aren’t so many zeroes
hive_pca <- prcomp(bee_heat_param_df %>% select(-c(...1, trial_n, type, days, n,
pBrood, pPollen, pHoney, pEmpty,
pBroodHeat, pPollenHeat, pHoneyHeat, pEmptyHeat,
broodMetric,pollenRing)),
center = TRUE,scale. = TRUE)
summary(hive_pca)
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10
Standard deviation 1.0451 1.0262 1.0251 1.0155 1.0043 0.99555 0.98359 0.97332 0.97070 0.95701
Proportion of Variance 0.1092 0.1053 0.1051 0.1031 0.1009 0.09911 0.09674 0.09474 0.09423 0.09159
Cumulative Proportion 0.1092 0.2145 0.3196 0.4227 0.5236 0.62271 0.71945 0.81419 0.90841 1.00000
hive_pca$rotation[,1:2]
PC1 PC2
rb 0.23749120 -0.25007941
rn -0.25023776 0.32573972
w -0.19436973 -0.10449372
pph 0.16339429 0.60044243
ph 0.41397553 -0.23493726
pp 0.01451974 0.24816931
k 0.44671832 0.18699667
bhd 0.51924059 0.17018363
wha 0.04183244 -0.52208919
qha -0.41712964 0.08132038
autoplot(hive_pca, colour = "pBroodHeat", loadings = TRUE, loadings.label = TRUE,
data = bee_heat_param_df)+
scale_color_viridis()+
theme_classic()
ggplot(bee_heat_param_df, aes(pPollen, pBroodHeat))+
geom_point()+
theme_classic()
ggplot(bee_heat_param_df, aes(wha, pPollen))+
geom_point()+
theme_classic()
ggplot(bee_heat_param_df, aes(pph, pp, color = pPollen))+
geom_point()+
scale_color_viridis()+
theme_classic()
heat_brood_glm <- glm(pBroodHeat ~ ., data = all_bee_param %>% dplyr::select(-c(...1, trial_n, type, days, n,
pBrood, pPollen, pHoney, pEmpty,
pPollenHeat, pHoneyHeat, pEmptyHeat,
broodMetric,pollenRing)))
stepped_model <- stepAIC(heat_brood_glm, direction = "both")
Start: AIC=-1644.08
pBroodHeat ~ rb + rn + w + pph + ph + pp + k + bhd + wha + qha
Df Deviance AIC
- wha 1 2.7657 -1646.0
- rb 1 2.7660 -1646.0
- k 1 2.7710 -1644.8
- pph 1 2.7733 -1644.3
<none> 2.7655 -1644.1
- rn 1 2.7757 -1643.7
- w 1 2.7808 -1642.5
- ph 1 2.7828 -1642.1
- pp 1 2.8025 -1637.6
- qha 1 3.6132 -1475.0
- bhd 1 4.1686 -1383.5
Step: AIC=-1646.02
pBroodHeat ~ rb + rn + w + pph + ph + pp + k + bhd + qha
Df Deviance AIC
- rb 1 2.7662 -1647.9
- k 1 2.7711 -1646.8
- pph 1 2.7739 -1646.2
<none> 2.7657 -1646.0
- rn 1 2.7759 -1645.7
- w 1 2.7812 -1644.5
+ wha 1 2.7655 -1644.1
- ph 1 2.7832 -1644.0
- pp 1 2.8028 -1639.5
- qha 1 3.6153 -1476.6
- bhd 1 4.1692 -1385.3
Step: AIC=-1647.9
pBroodHeat ~ rn + w + pph + ph + pp + k + bhd + qha
Df Deviance AIC
- k 1 2.7714 -1648.7
- pph 1 2.7743 -1648.0
<none> 2.7662 -1647.9
- rn 1 2.7764 -1647.5
- w 1 2.7821 -1646.2
+ rb 1 2.7657 -1646.0
- ph 1 2.7833 -1646.0
+ wha 1 2.7660 -1646.0
- pp 1 2.8029 -1641.5
- qha 1 3.6153 -1478.6
- bhd 1 4.1692 -1387.3
Step: AIC=-1648.71
pBroodHeat ~ rn + w + pph + ph + pp + bhd + qha
Df Deviance AIC
<none> 2.7714 -1648.7
- pph 1 2.7802 -1648.7
- rn 1 2.7807 -1648.6
+ k 1 2.7662 -1647.9
- ph 1 2.7879 -1646.9
+ rb 1 2.7711 -1646.8
+ wha 1 2.7713 -1646.7
- w 1 2.7888 -1646.7
- pp 1 2.8093 -1642.0
- qha 1 3.6258 -1478.7
- bhd 1 4.1708 -1389.1
Anova(stepped_model)
Analysis of Deviance Table (Type II tests)
Response: pBroodHeat
LR Chisq Df Pr(>Chisq)
rn 2.11 1 0.146522
w 3.96 1 0.046605 *
pph 2.00 1 0.157613
ph 3.77 1 0.052131 .
pp 8.63 1 0.003303 **
bhd 319.12 1 < 2.2e-16 ***
qha 194.83 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ggplot(all_bee_param, aes(bhd, pBroodHeat, color = pBroodHeat))+
geom_point()+
scale_color_viridis()+
theme_classic()
ggplot(all_bee_param, aes(qha, pBroodHeat, color = pBroodHeat))+
geom_point()+
scale_color_viridis()+
theme_classic()
now let’s see about the order of those models
bhd_m <- glm(pBroodHeat ~ bhd, data = all_bee_param)
bhd_2_m <- glm(pBroodHeat ~ poly(bhd,2), data = all_bee_param)
bhd_3_m <- glm(pBroodHeat ~ poly(bhd,3), data = all_bee_param)
bhd_ln_m <- glm(pBroodHeat ~ log(bhd), data = all_bee_param)
ICtab(bhd_m,bhd_2_m,bhd_3_m,bhd_ln_m)
dAIC df
bhd_2_m 0.0 4
bhd_3_m 1.3 5
bhd_ln_m 15.3 3
bhd_m 43.6 3
ggplot(all_bee_param, aes(bhd, pBroodHeat, color = pBroodHeat))+
geom_point()+
geom_smooth(method = "glm", formula = "y ~ poly(x,2)", se = FALSE) +
scale_color_viridis()+
theme_classic()
qha_m <- glm(pBroodHeat ~ qha, data = all_bee_param)
qha_2_m <- glm(pBroodHeat ~ poly(qha,2), data = all_bee_param)
qha_3_m <- glm(pBroodHeat ~ poly(qha,3), data = all_bee_param)
qha_ln_m <- glm(pBroodHeat ~ log(qha), data = all_bee_param)
ICtab(qha_m,qha_2_m,qha_3_m,qha_ln_m)
dAIC df
qha_m 0.0 3
qha_3_m 0.3 5
qha_2_m 1.9 4
qha_ln_m 31.9 3
ggplot(all_bee_param, aes(qha, pBroodHeat, color = pBroodHeat))+
geom_point()+
geom_smooth(method = "glm", formula = "y ~ x", se = FALSE) +
scale_color_viridis()+
theme_classic()
OLDER CODE, looking at model type, we decided on M2 ####
rand_bee_param <- read.csv("bee_data_isaac_rand.csv") %>% mutate(type = "Rand")
m1_bee_param <- read.csv("bee_data_isaac_m1.csv") %>% mutate(type = "M1") %>% select(-trial_n)
m2_bee_param <- read.csv("bee_data_isaac_m2.csv") %>% mutate(type = "M2") %>% select(-trial_n)
m2E_bee_param <- read.csv("bee_data_isaac_m2_empty.csv") %>% mutate(type = "M2E") %>% select(-trial_n)
m2K_bee_param <- read.csv("bee_data_isaac_m2_katie.csv") %>% mutate(type = "M2K") %>% select(-trial_n)
bee_param_df <- rbind(m1_bee_param,m2_bee_param) %>% rbind(.,rand_bee_param) %>% rbind(.,m2E_bee_param) %>% rbind(.,m2K_bee_param)
bee_param_df_perc <- bee_param_df %>% select(c(type,pBrood,pHoney,pPollen,pEmpty)) %>%
pivot_longer(c(pBrood,pHoney,pPollen,pEmpty))
ggplot(bee_param_df, aes(x = broodMetric, y = pollenRing, color = type))+
geom_point(alpha = 0.25)+
ylim(0,16)+
xlim(0,6)+
theme_classic()
ggplot(bee_param_df_perc, aes(x = type, y = value, fill = name))+
geom_boxplot()+
theme_classic()
ggplot(m1_bee_param, aes(x = pPollen, y = pph))+
geom_point()+
theme_classic()
broodMetricGLM_m0 <- glm(broodMetric ~ 1, data = m2_bee_param)
broodMetricGLM_m1 <- glm(broodMetric ~ n + rb + rn + w + pph + ph + pp + k, data = m2_bee_param)
broodMetricGLM_step <- step(broodMetricGLM_m0, direction = "both", scope = formula(broodMetricGLM_m1), trace = 0)
summary(broodMetricGLM_step)
pollenRingGLM_m0 <- glm(pollenRing ~ 1, data = m2_bee_param)
pollenRingGLM_m1 <- glm(pollenRing ~ n + rb + rn + w + pph + ph + pp + k, data = m2_bee_param)
pollenRingGLM_step <- step(pollenRingGLM_m0, direction = "both", scope = formula(pollenRingGLM_m1), trace = 0)
summary(pollenRingGLM_step)
queen_pos_df <- read.csv("queen_pos_data_0_5.csv")
queen_pos_df_heatmap <- queen_pos_df %>% mutate(QueenXRound = round_any(QueenX,5),
QueenYRound = round_any(QueenY,5)) %>%
group_by(QueenXRound,QueenYRound) %>%
summarise(meanAngleMoved = mean(AngleMoved),
count = n())
ggplot(queen_pos_df, aes(x = QueenX, y = QueenY))+
geom_path()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()
ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = Dist2Heat))+
geom_point()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()
ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = cos(AngleOppHeat)))+
geom_point()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()
ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = sin(AngleOppHeat)))+
geom_point()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()
ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = cos(meanAngleMoved)))+
geom_point(size = 5)+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()
ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = sin(meanAngleMoved)))+
geom_point(size = 5)+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()
ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = count))+
geom_point(size = 5)+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()